The rapid expansion of the e-commerce industry, accelerated by the COVID-19 pandemic, has led to a significant increase in digital fraud and associated financial losses. To maintain a healthy e-commerce ecosystem, robust cybersecurity and anti- fraud measures are essential. However, research on fraud detection systems has struggled to keep pace due to limited real- world datasets. Advances in artificial intelligence (AI), machine learning (ML), and cloud computing have revitalized research and applications in this domain. While ML and data mining techniques are popular in fraud detection, specific reviews focusing on their application in e-commerce platforms like eBay and Facebook are lacking depth. Existing reviews provide broad overviews but fail to grasp the intricacies of ML algorithms in the e-commerce context. To bridge this gap, our study conducts a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA)